app.py

import torch
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image
import warnings
import gradio as gr
import os
from gradio_client import Client

# Disable warnings for cleaner output
transformers.logging.set_verbosity_error()
transformers.logging.disable_progress_bar()
warnings.filterwarnings('ignore')

# Set device - will use CUDA if available, otherwise CPU
device = 'cuda' if torch.cuda.is_available() else 'cpu'
torch.set_default_device(device)

# Model configuration
model_name = 'qnguyen3/nanoLLaVA-1.5'

print(f"Loading model {model_name} on {device}...")

# Create model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map='auto',
    trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(
    model_name,
    trust_remote_code=True)

print("Model loaded successfully!")

# Initialize the client for the test bot
chatter = "K00B404/transcript_image_generator"
chatbot_client = Client(chatter)

def analyze_character(image_path, analysis_type):
    """
    Analyze a character image for dramaturgical insights
    
    Args:
        image_path: Path to the character image
        analysis_type: Type of character analysis to perform
    
    Returns:
        str: The generated character analysis
    """
    # Load and process image
    try:
        image = Image.open(image_path).convert('RGB')
        # Resize image to 512x512
        image = image.resize((256, 256), Image.Resampling.LANCZOS)
        
        image_tensor = model.process_images([image], model.config).to(dtype=model.dtype)
    except Exception as e:
        return f"Error processing image: {str(e)}"
    
    # Create prompt based on analysis type
    if analysis_type == "full_analysis":
        prompt = ("Analyze this character as a dramaturg would. Describe their appearance, " 
                 "potential personality traits, character archetype, suitable roles, and how they might " 
                 "function within a dramatic narrative. Consider costume, posture, expression, and visual symbolism.")
    elif analysis_type == "archetype":
        prompt = ("Identify the potential character archetype(s) represented in this image. " 
                 "Consider both classical archetypes (hero, mentor, trickster, etc.) and modern " 
                 "interpretations. Explain your reasoning based on visual cues.")
    elif analysis_type == "historical_context":
        prompt = ("Analyze this character's appearance in terms of historical context. " 
                 "Identify the likely time period, cultural influences, and how these elements " 
                 "would influence the character's role in a dramatic work. Consider costume details, " 
                 "props, and stylistic elements.")
    else:
        prompt = "Describe this character in detail for dramatic casting purposes."
    
    # Format input for the model using ChatML format
    messages = [
        {"role": "system", "content": "You are an expert dramaturg with deep knowledge of character analysis, theatrical traditions, and visual storytelling."},
        {"role": "user", "content": f'<image>\n{prompt}'}
    ]
    
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    
    # Split text around image placeholder
    text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
    input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0)
    
    # Generate response
    try:
        # Modified generation approach to avoid the cache issue
        output_ids = model.generate(
            input_ids,
            images=image_tensor,
            max_new_tokens=1024,
            temperature=0.7,
            top_p=0.9,
            use_cache=False,  # Disable caching to avoid the error
            do_sample=True)   # Enable sampling for more creative outputs
        
        response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True).strip()
        return response
    except Exception as e:
        # Add fallback generation method if the first method fails
        try:
            print(f"First generation method failed with: {str(e)}. Trying fallback method...")
            # Alternate generation approach
            with torch.inference_mode():
                output = model.generate(
                    input_ids,
                    images=image_tensor,
                    max_new_tokens=1024,
                    do_sample=True,
                    top_p=0.9,
                    temperature=0.7,
                    eos_token_id=tokenizer.eos_token_id,
                    pad_token_id=tokenizer.pad_token_id if tokenizer.pad_token_id else tokenizer.eos_token_id
                )
            response = tokenizer.decode(output[0][input_ids.shape[1]:], skip_special_tokens=True).strip()
            return response
        except Exception as e2:
            return f"Error generating analysis: {str(e)}\nFallback also failed: {str(e2)}\n\nPlease try a different image or check model compatibility."

def chat_with_persona(message, history, system_message, max_tokens, temperature, top_p):
    """Function to interact with the chatbot API using the generated persona"""
    try:
        # Call the API with the current message and system prompt (persona)
        response = chatbot_client.predict(
            message=message,
            system_message=system_message,
            max_tokens=max_tokens,
            temperature=temperature,
            top_p=top_p,
            api_name="/chat"
        )
        return response
    except Exception as e:
        return f"Error communicating with the chatbot API: {str(e)}"

# Create Gradio interface
def create_ui():
    with gr.Blocks(title="Dramaturg Character Analyzer") as demo:
        # Store the current analysis result for sharing between tabs
        analysis_result = gr.State("")
        
        with gr.Tabs() as tabs:
            # First tab: Character analysis
            with gr.TabItem("Character Analysis"):
                gr.Markdown("# Dramaturg Character Analyzer")
                gr.Markdown("Upload a character image to receive a dramaturgical analysis")
                
                with gr.Row():
                    with gr.Column():
                        input_image = gr.Image(type="filepath", label="Upload Character Image")
                        analysis_type = gr.Radio(
                            ["full_analysis", "archetype", "historical_context", "basic_description"],
                            label="Analysis Type",
                            value="full_analysis"
                        )
                        analyze_btn = gr.Button("Analyze Character")
                    
                    with gr.Column():
                        output_text = gr.Textbox(label="Character Analysis", lines=20)
                        copy_to_test_btn = gr.Button("Copy to Test Bot", interactive=False)
                
                def update_analysis_result(result):
                    # Enable the copy button once we have a result
                    return result, True
                
                analyze_btn.click(
                    fn=analyze_character,
                    inputs=[input_image, analysis_type],
                    outputs=[output_text, copy_to_test_btn]
                )
                
                def copy_to_test(result):
                    # Update the state and switch to the test tab
                    return result, 1
                
                copy_to_test_btn.click(
                    fn=copy_to_test,
                    inputs=[output_text],
                    outputs=[analysis_result, tabs]
                )
            
            # Second tab: Test bot integration
            with gr.TabItem("Test Bot"):
                gr.Markdown("# Test Your Character Persona")
                gr.Markdown("The character analysis will be used as the system prompt for the test bot.")
                
                with gr.Row():
                    with gr.Column():
                        system_prompt = gr.Textbox(label="System Prompt (Character Persona)", lines=10)
                        
                        with gr.Row():
                            max_tokens = gr.Slider(minimum=100, maximum=4000, value=1000, step=100, label="Max Tokens")
                            temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")
                            top_p = gr.Slider(minimum=0.1, maximum=1.0, value=0.9, step=0.1, label="Top P")
                        
                        user_input = gr.Textbox(label="Your message", placeholder="Ask something about the character...")
                        send_btn = gr.Button("Send Message")
                    
                    with gr.Column():
                        chatbot = gr.Chatbot(label="Conversation")
                
                def update_system_prompt(result):
                    return result
                
                # Update the system prompt when switching to this tab with an analysis result
                demo.load(
                    fn=update_system_prompt,
                    inputs=[analysis_result],
                    outputs=[system_prompt]
                )
                
                # Chat history for the test bot
                chat_history = []
                
                def respond(message, history, system_message, max_tokens_val, temperature_val, top_p_val):
                    # Add the user message to history
                    history.append((message, ""))
                    
                    # Get response from the test bot
                    response = chat_with_persona(
                        message=message,
                        history=history,
                        system_message=system_message,
                        max_tokens=max_tokens_val,
                        temperature=temperature_val,
                        top_p=top_p_val
                    )
                    
                    # Update the last history item with the response
                    history[-1] = (message, response)
                    
                    return "", history
                
                send_btn.click(
                    fn=respond,
                    inputs=[user_input, chatbot, system_prompt, max_tokens, temperature, top_p],
                    outputs=[user_input, chatbot]
                )
                
                # Also trigger on pressing Enter in the input box
                user_input.submit(
                    fn=respond,
                    inputs=[user_input, chatbot, system_prompt, max_tokens, temperature, top_p],
                    outputs=[user_input, chatbot]
                )
        
    return demo

# Main function
if __name__ == "__main__":
    demo = create_ui()
    demo.launch(share=True)
    print("Dramaturg Character Analyzer is now running with Test Bot integration!")